'''
@author: zhangkai
@license: (C) Copyright 2017-2023
@contact: jeffcobile@gmail.com
@Software : PyCharm
@file: model.py
@time: 2021-09-15 11:58:05
@desc: 
'''
import torch
import math
import os
from PIL import ExifTags

for orientation in ExifTags.TAGS.keys():
    if ExifTags.TAGS[orientation] == 'Orientation':
        break


def decode(loc, priors, variances):
    """Decode locations from predictions using priors to undo
    the encoding we did for offset regression at train time.
    Args:
        loc (tensor): location predictions for loc layers,
            Shape: [num_priors,4]
        priors (tensor): Prior boxes in center-offset form.
            Shape: [num_priors,4].
        variances: (list[float]) Variances of priorboxes
    Return:
        decoded bounding box predictions
    """
    boxes = torch.cat((
        priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
        priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
    boxes[:, :2] -= boxes[:, 2:] / 2
    boxes[:, 2:] += boxes[:, :2]
    return boxes


def nms(boxes, scores, overlap=0.5, top_k=200):
    """Apply non-maximum suppression at test time to avoid detecting too many
    overlapping bounding boxes for a given object.
    Args:
        boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
        scores: (tensor) The class predscores for the img, Shape:[num_priors].
        overlap: (float) The overlap thresh for suppressing unnecessary boxes.
        top_k: (int) The Maximum number of box preds to consider.
    Return:
        The indices of the kept boxes with respect to num_priors.
    """

    keep = torch.Tensor(scores.size(0)).fill_(0).long()
    if boxes.numel() == 0:
        return keep
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]
    area = torch.mul(x2 - x1, y2 - y1)
    v, idx = scores.sort(0)  # sort in ascending order
    # I = I[v >= 0.01]
    idx = idx[-top_k:]  # indices of the top-k largest vals
    xx1 = boxes.new()
    yy1 = boxes.new()
    xx2 = boxes.new()
    yy2 = boxes.new()
    w = boxes.new()
    h = boxes.new()

    # keep = torch.Tensor()
    count = 0
    while idx.numel() > 0:
        i = idx[-1]  # index of current largest val
        # keep.append(i)
        keep[count] = i
        count += 1
        if idx.size(0) == 1:
            break
        idx = idx[:-1]  # remove kept element from view
        # load bboxes of next highest vals
        torch.index_select(x1, 0, idx, out=xx1)
        torch.index_select(y1, 0, idx, out=yy1)
        torch.index_select(x2, 0, idx, out=xx2)
        torch.index_select(y2, 0, idx, out=yy2)
        # store element-wise max with next highest score
        xx1 = torch.clamp(xx1, min=x1[i])
        yy1 = torch.clamp(yy1, min=y1[i])
        xx2 = torch.clamp(xx2, max=x2[i])
        yy2 = torch.clamp(yy2, max=y2[i])
        w.resize_as_(xx2)
        h.resize_as_(yy2)
        w = xx2 - xx1
        h = yy2 - yy1
        # check sizes of xx1 and xx2.. after each iteration
        w = torch.clamp(w, min=0.0)
        h = torch.clamp(h, min=0.0)
        inter = w*h
        # IoU = i / (area(a) + area(b) - i)
        rem_areas = torch.index_select(area, 0, idx)  # load remaining areas)
        union = (rem_areas - inter) + area[i]
        IoU = inter/union  # store result in iou
        # keep only elements with an IoU <= overlap
        idx = idx[IoU.le(overlap)]
    return keep, count


def diounms(boxes, scores, overlap=0.5, top_k=200, beta1=1.0):
    """Apply DIoU-NMS at test time to avoid detecting too many
    overlapping bounding boxes for a given object.
    Args:
        boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
        scores: (tensor) The class predscores for the img, Shape:[num_priors].
        overlap: (float) The overlap thresh for suppressing unnecessary boxes.
        top_k: (int) The Maximum number of box preds to consider.
        beta1: (float) DIoU=IoU-R_DIoU^{beta1}.
    Return:
        The indices of the kept boxes with respect to num_priors.
    """

    keep = scores.new(scores.size(0)).zero_().long()
    if boxes.numel() == 0:
        return keep
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]
    area = torch.mul(x2 - x1, y2 - y1)
    v, idx = scores.sort(0)  # sort in ascending order
    # I = I[v >= 0.01]
    idx = idx[-top_k:]  # indices of the top-k largest vals
    xx1 = boxes.new()
    yy1 = boxes.new()
    xx2 = boxes.new()
    yy2 = boxes.new()
    w = boxes.new()
    h = boxes.new()

    # keep = torch.Tensor()
    count = 0
    while idx.numel() > 0:
        i = idx[-1]  # index of current largest val
        # keep.append(i)
        keep[count] = i
        count += 1
        if idx.size(0) == 1:
            break
        idx = idx[:-1]  # remove kept element from view
        # load bboxes of next highest vals
        torch.index_select(x1, 0, idx, out=xx1)
        torch.index_select(y1, 0, idx, out=yy1)
        torch.index_select(x2, 0, idx, out=xx2)
        torch.index_select(y2, 0, idx, out=yy2)
        # store element-wise max with next highest score
        inx1 = torch.clamp(xx1, min=x1[i])
        iny1 = torch.clamp(yy1, min=y1[i])
        inx2 = torch.clamp(xx2, max=x2[i])
        iny2 = torch.clamp(yy2, max=y2[i])
        center_x1 = (x1[i] + x2[i]) / 2
        center_y1 = (y1[i] + y2[i]) / 2
        center_x2 = (xx1 + xx2) / 2
        center_y2 = (yy2 + yy2) / 2
        d = (center_x1 - center_x2) ** 2 + (center_y1 - center_y2) ** 2
        cx1 = torch.clamp(xx1, max=x1[i])
        cy1 = torch.clamp(yy1, max=y1[i])
        cx2 = torch.clamp(xx2, min=x2[i])
        cy2 = torch.clamp(yy2, min=y2[i])
        c = (cx2 - cx1) ** 2 + (cy2 - cy1) ** 2
        u= d / c
        w.resize_as_(xx2)
        h.resize_as_(yy2)
        w = inx2 - inx1
        h = iny2 - iny1
        # check sizes of xx1 and xx2.. after each iteration
        w = torch.clamp(w, min=0.0)
        h = torch.clamp(h, min=0.0)
        inter = w*h
        # IoU = i / (area(a) + area(b) - i)
        rem_areas = torch.index_select(area, 0, idx)  # load remaining areas)
        union = (rem_areas - inter) + area[i]
        IoU = inter/union - u ** beta1 # store result in diou
        # keep only elements with an IoU <= overlap
        idx = idx[IoU.le(overlap)]
    return keep, count


def match(threshold, truths, priors, variances, labels, loc_t, conf_t, idx):
    """Match each prior box with the ground truth box of the highest jaccard
    overlap, encode the bounding boxes, then return the matched indices
    corresponding to both confidence and location preds.
    Args:
        threshold: (float) The overlap threshold used when mathing boxes.
        truths: (tensor) Ground truth boxes, Shape: [num_obj, num_priors].
        priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
        variances: (tensor) Variances corresponding to each prior coord,
            Shape: [num_priors, 4].
        labels: (tensor) All the class labels for the image, Shape: [num_obj].
        loc_t: (tensor) Tensor to be filled w/ endcoded location targets.
        conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
        idx: (int) current batch index
    Return:
        The matched indices corresponding to 1)location and 2)confidence preds.
    """
    # jaccard index
    '''
    overlaps是[n,11620]，n表示有n个Ground Truth Box,每一个值的含义是所对应的priorbox与当前Ground Truth Box的IOU
    如果这张图片上有三个Ground Truth Box，那么overlaps的shape就是[3,11620]，每一行有11620个值，每个值代表的是每一个prior box(共11620个)
    与第一个Ground Truth box的IOU值，同样第二行，第三行也是如此
    '''
    overlaps = jaccard(
        truths,
        point_form(priors) # 由于priors的坐标是cx,cy,width,height,所以需要转化为xmin,ymin,xmax,ymax
    )

    '''
    best_prior_overlap：shape是[1,num_objects]，num_objects就是当前图片上Ground Truth Box的个数,假设num_objects=3，那么它的shape
    就是[1,3]，第一个值得含义就是这11620个prior box与第一个Ground Truth box的IOU的最大值，同样第二个值，第三个值也是如此。
    best_prior_idx：shape也是[1,num_objects]，第一个值就是与当前Ground Truth Box比较获得最大IOU值得那个Prior box的index，同样后面的值得含义也是如此
    '''
    best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)

    '''
    best_truth_overlap：shape是[1,11620]，第一个值得含义是第一个Proir Box与当前图片所有的Ground Truth Box比较所获得的的最大IOU，后面的值得含义也如此
    best_truth_idx：shape是[1,11620]，值得含义就是best_truth_overlap中获得IOU最大值所对应的的Ground Truth Box的index
    '''
    best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)

    best_truth_idx.squeeze_(0)
    best_truth_overlap.squeeze_(0)
    best_prior_idx.squeeze_(1)
    best_prior_overlap.squeeze_(1)

    '''
    根据上面的分析，我们知道best_prior_idx里面包含的值表明的是跟Ground Truth Box能获得到最大IOU的那些Prior Box的index，这一步是要将
    best_truth_overlap里面的那些对应位置的值设置为2.best_truth_overlap的值原先都是小于等于1的(因为是IOU),做完这个操作之后，有num_objects
    个值是2了。
    '''
    best_truth_overlap.index_fill_(0, best_prior_idx, 2)  # ensure best prior

    # ensure every gt matches with its prior of max overlap
    for j in range(best_prior_idx.size(0)):
        best_truth_idx[best_prior_idx[j]] = j

    matches = truths[best_truth_idx]          # Shape: [11620,4] 跟当前对应位置锚点的IOU最大的ground truth box的坐标
    conf = labels[best_truth_idx]          # Shape: [11620] 跟当前对应位置锚点的IOU最大的ground truth box的label
    conf[best_truth_overlap < threshold] = 0  # label as background 小于阈值的位置label都变成背景
    # 得到一张图的真值框与锚的坐标偏移值[11620, 4]
    loc = encode(matches, priors, variances)

    # 保存一个batch中的坐标偏移值和分类误差
    loc_t[idx] = loc    # [num_priors,4] encoded offsets to learn
    conf_t[idx] = conf  # [num_priors] top class label for each prior


def match_ious(threshold, truths, priors, variances, labels, loc_t, conf_t, idx):
    """Match each prior box with the ground truth box of the highest jaccard
    overlap, encode the bounding boxes, then return the matched indices
    corresponding to both confidence and location preds.
    Args:
        threshold: (float) The overlap threshold used when mathing boxes.
        truths: (tensor) Ground truth boxes, Shape: [num_obj, num_priors].
        priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
        variances: (tensor) Variances corresponding to each prior coord,
            Shape: [num_priors, 4].
        labels: (tensor) All the class labels for the image, Shape: [num_obj].
        loc_t: (tensor) Tensor to be filled w/ endcoded location targets.
        conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
        idx: (int) current batch index
    Return:
        The matched indices corresponding to 1)location and 2)confidence preds.
    """
    # jaccard index
    loc_t[idx] = point_form(priors)
    overlaps = jaccard(
        truths,
        point_form(priors)
    )
    # (Bipartite Matching)
    # [1,num_objects] best prior for each ground truth
    best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
    # [1,num_priors] best ground truth for each prior
    best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)
    best_truth_idx.squeeze_(0)
    best_truth_overlap.squeeze_(0)
    best_prior_idx.squeeze_(1)
    best_prior_overlap.squeeze_(1)

    best_truth_overlap.index_fill_(0, best_prior_idx, 2)  # ensure best prior

    # TODO refactor: index  best_prior_idx with long tensor
    # ensure every gt matches with its prior of max overlap
    for j in range(best_prior_idx.size(0)):
        best_truth_idx[best_prior_idx[j]] = j

    matches = truths[best_truth_idx]          # Shape: [num_priors,4]
    conf = labels[best_truth_idx]         # Shape: [num_priors]

    conf[best_truth_overlap < threshold] = 0  # label as background
    loc_t[idx] = matches    # [num_priors,4] encoded offsets to learn
    conf_t[idx] = conf  # [num_priors] top class label for each prior


def log_sum_exp(x):
    """Utility function for computing log_sum_exp while determining
    This will be used to determine unaveraged confidence loss across
    all examples in a batch.
    Args:
        x (Variable(tensor)): conf_preds from conf layers
    """
    x_max = x.data.max()
    return torch.log(torch.sum(torch.exp(x-x_max), 1, keepdim=True)) + x_max


def bbox_overlaps_iou(bboxes1, bboxes2):
    rows = bboxes1.shape[0]
    cols = bboxes2.shape[0]
    ious = torch.zeros((rows, cols))
    if rows * cols == 0:
        return ious
    exchange = False
    if bboxes1.shape[0] > bboxes2.shape[0]:
        bboxes1, bboxes2 = bboxes2, bboxes1
        ious = torch.zeros((cols, rows))
        exchange = True
    area1 = (bboxes1[:, 2] - bboxes1[:, 0]) * (
        bboxes1[:, 3] - bboxes1[:, 1])
    area2 = (bboxes2[:, 2] - bboxes2[:, 0]) * (
        bboxes2[:, 3] - bboxes2[:, 1])

    inter_max_xy = torch.min(bboxes1[:, 2:],bboxes2[:, 2:])
    inter_min_xy = torch.max(bboxes1[:, :2],bboxes2[:, :2])

    inter = torch.clamp((inter_max_xy - inter_min_xy), min=0)
    inter_area = inter[:, 0] * inter[:, 1]
    union = area1+area2-inter_area
    ious = inter_area / union
    ious = torch.clamp(ious,min=0,max = 1.0)
    if exchange:
        ious = ious.T
    return ious


def bbox_overlaps_giou(bboxes1, bboxes2):
    rows = bboxes1.shape[0]
    cols = bboxes2.shape[0]
    ious = torch.zeros((rows, cols))
    if rows * cols == 0:
        return ious
    exchange = False
    if bboxes1.shape[0] > bboxes2.shape[0]:
        bboxes1, bboxes2 = bboxes2, bboxes1
        ious = torch.zeros((cols, rows))
        exchange = True
    area1 = (bboxes1[:, 2] - bboxes1[:, 0]) * (
        bboxes1[:, 3] - bboxes1[:, 1])
    area2 = (bboxes2[:, 2] - bboxes2[:, 0]) * (
        bboxes2[:, 3] - bboxes2[:, 1])

    inter_max_xy = torch.min(bboxes1[:, 2:],bboxes2[:, 2:])

    inter_min_xy = torch.max(bboxes1[:, :2],bboxes2[:, :2])

    out_max_xy = torch.max(bboxes1[:, 2:],bboxes2[:, 2:])

    out_min_xy = torch.min(bboxes1[:, :2],bboxes2[:, :2])

    inter = torch.clamp((inter_max_xy - inter_min_xy), min=0)
    inter_area = inter[:, 0] * inter[:, 1]
    outer = torch.clamp((out_max_xy - out_min_xy), min=0)
    outer_area = outer[:, 0] * outer[:, 1]
    union = area1+area2-inter_area
    closure = outer_area

    ious = inter_area / union - (closure - union) / closure
    ious = torch.clamp(ious,min=-1.0,max = 1.0)
    if exchange:
        ious = ious.T
    return ious


def bbox_overlaps_ciou(bboxes1, bboxes2):
    rows = bboxes1.shape[0]
    cols = bboxes2.shape[0]
    cious = torch.zeros((rows, cols))
    if rows * cols == 0:
        return cious
    exchange = False
    if bboxes1.shape[0] > bboxes2.shape[0]:
        bboxes1, bboxes2 = bboxes2, bboxes1
        cious = torch.zeros((cols, rows))
        exchange = True

    w1 = bboxes1[:, 2] - bboxes1[:, 0]
    h1 = bboxes1[:, 3] - bboxes1[:, 1]
    w2 = bboxes2[:, 2] - bboxes2[:, 0]
    h2 = bboxes2[:, 3] - bboxes2[:, 1]

    area1 = w1 * h1
    area2 = w2 * h2

    center_x1 = (bboxes1[:, 2] + bboxes1[:, 0]) / 2
    center_y1 = (bboxes1[:, 3] + bboxes1[:, 1]) / 2
    center_x2 = (bboxes2[:, 2] + bboxes2[:, 0]) / 2
    center_y2 = (bboxes2[:, 3] + bboxes2[:, 1]) / 2

    inter_max_xy = torch.min(bboxes1[:, 2:],bboxes2[:, 2:])
    inter_min_xy = torch.max(bboxes1[:, :2],bboxes2[:, :2])
    out_max_xy = torch.max(bboxes1[:, 2:],bboxes2[:, 2:])
    out_min_xy = torch.min(bboxes1[:, :2],bboxes2[:, :2])

    inter = torch.clamp((inter_max_xy - inter_min_xy), min=0)
    inter_area = inter[:, 0] * inter[:, 1]
    inter_diag = (center_x2 - center_x1)**2 + (center_y2 - center_y1)**2
    outer = torch.clamp((out_max_xy - out_min_xy), min=0)
    outer_diag = (outer[:, 0] ** 2) + (outer[:, 1] ** 2)
    union = area1+area2-inter_area
    u = (inter_diag) / outer_diag
    iou = inter_area / union
    with torch.no_grad():
        arctan = torch.atan(w2 / h2) - torch.atan(w1 / h1)
        v = (4 / (math.pi ** 2)) * torch.pow((torch.atan(w2 / h2) - torch.atan(w1 / h1)), 2)
        S = 1 - iou
        alpha = v / (S + v)
        w_temp = 2 * w1
    ar = (8 / (math.pi ** 2)) * arctan * ((w1 - w_temp) * h1)
    cious = iou - (u + alpha * ar)
    cious = torch.clamp(cious,min=-1.0,max = 1.0)
    if exchange:
        cious = cious.T
    return cious


def bbox_overlaps_diou(bboxes1, bboxes2):

    rows = bboxes1.shape[0]
    cols = bboxes2.shape[0]
    dious = torch.zeros((rows, cols))
    if rows * cols == 0:
        return dious
    exchange = False
    if bboxes1.shape[0] > bboxes2.shape[0]:
        bboxes1, bboxes2 = bboxes2, bboxes1
        dious = torch.zeros((cols, rows))
        exchange = True

    w1 = bboxes1[:, 2] - bboxes1[:, 0]
    h1 = bboxes1[:, 3] - bboxes1[:, 1]
    w2 = bboxes2[:, 2] - bboxes2[:, 0]
    h2 = bboxes2[:, 3] - bboxes2[:, 1]

    area1 = w1 * h1
    area2 = w2 * h2
    center_x1 = (bboxes1[:, 2] + bboxes1[:, 0]) / 2
    center_y1 = (bboxes1[:, 3] + bboxes1[:, 1]) / 2
    center_x2 = (bboxes2[:, 2] + bboxes2[:, 0]) / 2
    center_y2 = (bboxes2[:, 3] + bboxes2[:, 1]) / 2

    inter_max_xy = torch.min(bboxes1[:, 2:],bboxes2[:, 2:])
    inter_min_xy = torch.max(bboxes1[:, :2],bboxes2[:, :2])
    out_max_xy = torch.max(bboxes1[:, 2:],bboxes2[:, 2:])
    out_min_xy = torch.min(bboxes1[:, :2],bboxes2[:, :2])

    inter = torch.clamp((inter_max_xy - inter_min_xy), min=0)
    inter_area = inter[:, 0] * inter[:, 1]
    inter_diag = (center_x2 - center_x1)**2 + (center_y2 - center_y1)**2
    outer = torch.clamp((out_max_xy - out_min_xy), min=0)
    outer_diag = (outer[:, 0] ** 2) + (outer[:, 1] ** 2)
    union = area1+area2-inter_area
    dious = inter_area / union - (inter_diag) / outer_diag
    dious = torch.clamp(dious,min=-1.0,max = 1.0)
    if exchange:
        dious = dious.T
    return dious


def jaccard(box_a, box_b):
    """Compute the jaccard overlap of two sets of boxes.  The jaccard overlap
    is simply the intersection over union of two boxes.  Here we operate on
    ground truth boxes and default boxes.
    E.g.:
        A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
    Args:
        box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
        box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
    Return:
        jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
    """
    inter = intersect(box_a, box_b)
    area_a = ((box_a[:, 2]-box_a[:, 0]) *
              (box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter)  # [A,B]
    area_b = ((box_b[:, 2]-box_b[:, 0]) *
              (box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter)  # [A,B]
    union = area_a + area_b - inter
    return inter / union  # [A,B]


def intersect(box_a, box_b):
    """ We resize both tensors to [A,B,2] without new malloc:
    [A,2] -> [A,1,2] -> [A,B,2]
    [B,2] -> [1,B,2] -> [A,B,2]
    Then we compute the area of intersect between box_a and box_b.
    Args:
      box_a: (tensor) bounding boxes, Shape: [A,4].
      box_b: (tensor) bounding boxes, Shape: [B,4].
    Return:
      (tensor) intersection area, Shape: [A,B].
    """
    A = box_a.size(0)
    B = box_b.size(0)
    max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2),
                       box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
    min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2),
                       box_b[:, :2].unsqueeze(0).expand(A, B, 2))
    inter = torch.clamp((max_xy - min_xy), min=0)
    return inter[:, :, 0] * inter[:, :, 1]


def point_form(boxes):
    """ Convert prior_boxes to (xmin, ymin, xmax, ymax)
    representation for comparison to point form ground truth data.
    Args:
        boxes: (tensor) center-size default boxes from priorbox layers.
    Return:
        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
    """
    return torch.cat((boxes[:, :2] - boxes[:, 2:]/2,     # xmin, ymin
                      boxes[:, :2] + boxes[:, 2:]/2), 1)  # xmax, ymax


def encode(matched, priors, variances):
    '''
    将来自锚框层的方差编码到与这些锚框相匹配到的真值框中
    :param matched:
    :param priors:
    :param variances:
    :return:
    '''
    """Encode the variances from the priorbox layers into the ground truth boxes
    we have matched (based on jaccard overlap) with the prior boxes.
    Args:
        matched: (tensor) Coords of ground truth for each prior in point-form
            Shape: [num_priors, 4].
        priors: (tensor) Prior boxes in center-offset form
            Shape: [num_priors,4].
        variances: (list[float]) Variances of priorboxes
    Return:
        encoded boxes (tensor), Shape: [num_priors, 4]
    """

    # dist b/t match center and prior's center
    g_cxcy = (matched[:, :2] + matched[:, 2:])/2 - priors[:, :2]
    # encode variance
    g_cxcy /= (variances[0] * priors[:, 2:])
    # match wh / prior wh
    g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
    g_wh = torch.log(g_wh) / variances[1]
    # return target for smooth_l1_loss
    return torch.cat([g_cxcy, g_wh], 1)  # [num_priors,4]


def exif_size(img):
    # Returns exif-corrected PIL size
    s = img.size  # (width, height)
    try:
        rotation = dict(img._getexif().items())[orientation]
        if rotation == 6:  # rotation 270
            s = (s[1], s[0])
        elif rotation == 8:  # rotation 90
            s = (s[1], s[0])
    except:
        pass

    return s


def get_hash(files):
    # Returns a single hash value of a list of files
    return sum(os.path.getsize(f) for f in files if os.path.isfile(f))